Spatiotemporal retrieval of dynamic video object trajectories in geographical scenes

Current studies on video trajectory retrieval focus on the retrieval and analysis of image content, neglecting the gap between the spatiotemporal continuity of retrieval conditions and the spatiotemporal discontinuity of multi‐camera video trajectories. In this study, we propose a method for the spatiotemporal retrieval of dynamic video object trajectories in geographic scenes. Based on the camera calibration, the proposed method organizes the scene, cameras, and trajectories, constructs the spatiotemporal constraints, and queries the trajectories using two measures: camera‐by‐camera retrieval and global trajectory retrieval. The proposed method was verified through experiments, and the results demonstrate that both measures can query trajectories effectively and reduce the spatiotemporal video review range under different spatiotemporal constraints. Furthermore, compared with camera‐by‐camera retrieval, global trajectory retrieval can reduce the spatiotemporal video review range further and return more accurate results. The proposed method may provide support for the spatial analysis and understanding of surveillance video data.     

An Event‐driven Spatiotemporal Data Model (E‐ST) Supporting Dynamic Expression and Simulation of Geographic Processes

The wide use of various sensors makes real‐time data acquisition possible. A new spatiotemporal data model, the Event‐driven Spatiotemporal Data Model (E‐ST), is proposed to dynamically express and simulate the spatiotemporal processes of geographic phenomena. In E‐ST, a sensor object is introduced into the model as a flexible real‐time data source. An event type that is generating and driving conditions is registered into a geographic object, so an event can not only express spatiotemporal change in a geographic object, but also drive spatiotemporal change in some geographic objects. As a dynamic GIS data model, the E‐ST has five characteristics – Temporality and Spatiality, Real‐time, Extendability, Causality, and Realizability. Described and realized in UML, a test‐case deployment demonstrating the impact of urban waterlogging on traffic confirms that a spatiotemporal change process in a geographic phenomena is expressed and simulated by this model. Summarizing this work, four directions for future research are outlined.     

Spatial‐Scene Similarity Queries

Assessing spatial scenes for similarity is difficult from a cognitive and computational perspective. Solutions to spatial‐scene similarity assessments are sensible only if corresponding elements in the compared scenes are identified correctly. This matching process becomes increasingly complex and error‐prone for large spatial scenes as it is questionable how to choose one set of associations over another or how to account quantitatively for unmatched elements. We develop a comprehensive methodology for similarity queries over spatial scenes that incorporates cognitively motivated approaches about scene comparisons, together with explicit domain knowledge about spatial objects and their relations for the relaxation of spatial query constraints. Along with a sound graph‐theoretical methodology, this approach provides the foundation for plausible reasoning about spatial‐scene similarity queries.     

A Similarity-Based Approach for Improving the Efficiency of Drawing Spatiotemporal Point Features

ABSTRACT

The mapping of spatiotemporal point features plays an important role in geovisualization. However, such mapping suffers from low efficiency due to computational redundancy when similar symbols are used to visualize spatiotemporal point features. This paper presents a similarity-based approach to predict and avoid computational redundancy, which improves mapping efficiency. First, to identify computational redundancy, the similarity of point symbols is measured based on commonalities in symbol graphics and symbol drawing operations. Second, a similarity-enhanced method is proposed to comprehensively predict and avoid computational redundancies when mapping spatiotemporal point features. This approach was tested using two real-world spatiotemporal datasets. The results suggest that the proposed approach offers relatively large performance improvements.     

A Morphological Approach to Predicting Urban Expansion

Many methods for modeling urban expansion are available. Most of these computational models demand a variety of large‐scale environmental and socio‐economic data to investigate the relationship between urban expansion and its driving forces. These requirements are not always fulfilled, particularly in developing countries due to a lack of data availability. This necessitates methods not suffering from data limitations to ease their application. Consequently, this research presents a morphological approach for predicting urban expansion on the basis of spatiotemporal dynamics of urban margins by investigating the interior metropolitan area of Tehran, Iran as a case study. To assess the model's performance, urban expansion is monitored from 1976 to 2012. The proposed model is evaluated to ensure that the prediction performance for the year 2012 is acceptable. For the year 2024, the model predicts Tehran's urban expansion at an overall R² of 88%. Accordingly, it is concluded that: (1) although this approach only inputs urban margins, it represents a suitable and easy‐to‐use urban expansion model; and (2) urban planners are faced with continuing urban expansion.     

基于历史拓扑和描述子的时空数据模型

针对已有时空数据模型在表达时空信息上存在的问题,提出基于一个新的历史拓扑和描述子的时空数据模型STORM.STORM采用结合显式表达和隐式表达的混合型方法来表示时空信息,其中历史拓扑显式表达时空对象间的历史关联,而描述子隐式表达单个时空对象的内部属性变化以及时空对象间的空间拓扑关系.该模型可以同时表达时空信息的静态语义和动态语义,并可通过扩展的关系结构和关系代数操作进行表示和查询.     

Multiple intra-urban land use simulations and driving factors analysis: a case study in Huicheng,China

Simulations of intra-urban land use changes have gradually attracted more attention as these approaches are extremely helpful in regard to decision making and policy formulation. While prior studies mostly focused on methods of developing intra-urban level simulations, very little research has been conducted explain the factors driving intra-urban land use change. Urban planners are highly concerned with how inner-city structures are formed and how they function. Here, to simulate multiple intra-urban land use changes and to identify the contribution of different driving factors, we developed a random forests (RF) algorithm-based cellular automata (CA) simulation model. In this study, the model applied diverse categories of spatial variables, including traffic location factors, environmental factors, public services, and population density, as the driving factors to enhance our understanding of the dynamics of internal urban land use. The CA model was tested using data from the Huicheng district of Huizhou city in the Guangdong province of China. The Model was validated using actual historical land use data from 2000 to 2010. By applying the validated model, multiple intra-urban land use maps were simulated for 2015. Simultaneously, spatial variable importance measures (VIMs) were calculated by using the out-of-bag (OOB) error estimation approach of the RF algorithm. Based on the calculation results, we assessed and analysed the significance of each intra-urban land use driver for this region. This study provides urban planners and relevant scholars with detailed and targeted information that can aid in the formulation of specific planning strategies for different intra-urban land uses and support the future evolution of this area.     

城市典型要素遥感智能监测与模拟推演关键技术EI北大核心CSCD

城市典型要素遥感智能监测与模拟推演的理论、方法与应用,对于国土空间规划与管理,城市规划与综合治理,区域决策与管理等均具有关键支撑作用。针对覆盖要素和驱动要素复杂非线性,本文研发了协同多源遥感数据的智能识别方法,实现了精细化高可信覆盖要素分类;协同遥感、POI兴趣点和时空大数据等多源数据,有效探测和识别了要素变动的驱动力。在此基础上,开展了空间演变机理挖掘、空间统计建模、启发式智能建模,并应用于土地利用、城市扩张、生态演变、碳储量等。同时,研发了聚焦城市生长推演的UrbanCA平台以及聚焦多类土地利用变化推演的Futureland平台,集成了自主研发的模拟推演系列方法并以长三角为主要区域进行了验证。     

An integrated approach for modelling and global registration of point clouds

Point cloud acquisition by using laser scanners provides an efficient way for 3D as-built modelling of industrial installations. Covering such an installation with point cloud data often requires data acquisition from multiple standpoints. Before the actual modelling can start the transformation parameters of all scans need to be determined. Two methods to register point clouds of industrial scenes with different coordinate definitions are presented. Corresponding object models in different scans are used to determine the translation and rotation parameters of the scans. The first method, called Indirect method, is a two-step approach as object fitting and registration of the scenes is done separately. The second method, called Direct method simultaneously determines the shape and pose parameters of the objects as well as the registration parameters. Both methods are designed such that optimal use can be made of the knowledge of shapes present in industrial environments. Compared to ICP the presented approach combines registration and modelling and thus avoids the accumulation of errors. Furthermore, the simultaneous registration of multiple scans is possible. The presented approaches are based on non-linear least squares and provide quality measures in the form of covariance matrix of the estimated parameters, which can be used to decide if more scans are needed, and how and where they should be captured. Results are presented on some point cloud data-sets from actual industrial sites, where registration was done without using any artificial targets.     

Spatiotemporal analysis of photo contribution patterns to Panoramio and Flickr

ABSTRACT

Photo-sharing services provide a rich resource of crowdsourced spatial data consisting of georeferenced imagery and metadata. Shared photos can provide valuable information for a variety of applications and geospatial analysis tasks, such as identifying tourist hot spots or traveled routes. Understanding the spatiotemporal patterns of photo contributions will allow analysts to assess the suitability of these data for related analysis tasks. Using California as a study area, this paper analyzes various aspects of photo contribution patterns of Panoramio and Flickr. It identifies areas where annual photo contributions are still growing and areas that undergo a decline in annual contributions. Multiple regression is used to identify which environmental correlates are associated with an increase in photo-sharing activities. Furthermore, panel data of annual contributions between 2006 and 2013 for California subcounties will be used in a regression model to demonstrate that there is a positive feedback effect between Panoramio and Flickr photo contributions, but no neighborhood effect. The results of this paper provide insight into the data quality of crowdsourced image collections. These collections are commonly used for geospatial applications, including tourist information services and the computation of scenic routes.     

Evaluation of the quality of an online geocoding resource in the context of a large Brazilian city

Geocoding urban addresses usually requires the use of an underlying address database. Under the influence of the format defined for TIGER files decades ago, most address databases and street geocoding algorithms are organized around street centerlines, associating numbering ranges to thoroughfare segments between two street crossings. While this method has been successfully employed in the USA for a long time, its transposition to other countries may lead to increased errors. This article presents an evaluation of the centerline‐geocoding resources provided by Google Maps, as compared to the point‐geocoding method used in the city of Belo Horizonte, Brazil, which we took as a baseline. We generated a textual address for each point object found in the city's point‐based address database, and submitted it to the Google Maps geocoding API. We then compared the resulting coordinates with the ones recorded in Belo Horizonte's GIS. We demonstrate that the centerline segment interpolation method, employed by the online resources following the American practice, has problems that can considerably influence the quality of the geocoding outcome. Completeness and accuracy have been found to be irregular, especially within lower income areas. Such errors in online services can have a significant impact on geocoding efforts related to social applications, such as public health and education, since the online service can be faulty and error‐prone in the most socially demanding areas of the city. In the conclusion, we point out that a volunteered geographic information (VGI) approach can help with the enrichment and enhancement of current geocoding resources, and can possibly lead to their transformation into more reliable point‐based geocoding services.     

利用松弛标记法进行空间场景匹配

相似性度量是地理学中的关键组成部分,被广泛应用于空间检索、空间信息整合及空间数据挖掘中。因为空间场景中实体个数的差异及空间对象间的关系难以精确相等,若执行空间场景的完全精确匹配,可能会使得检索结果为空。顾及尺度差异,从空间场景中进行空间语义理解,建立了多尺度空间场景的形式化描述模型,并提取场景中稳定的特征构建空间场景特征矩阵。建立场景间的初始匹配概率矩阵后,基于松弛标记法迭代更新概率矩阵,直到矩阵收敛于一全局最小值并确定匹配的实体对,从而进行空间场景相似性评估。采用武汉居民地域数据进行场景匹配实验,并对不同邻域搜索半径下的匹配时间及精确度进行对比与分析,实验结果表明,基于松弛标记法的空间场景匹配方法具有较高的精确度。     

矿山三维场景漫游系统的设计与实现

基于三维建模软件Multigen Creator Pro，结合矿山项目需求，研究了矿山三维场景建立的方法，成功建立了视觉效果较好的矿山三维场景，并利用仿真软件Vega Prime和组件GIS—MapObjects，对三维场景的渲染、驱动和二维三维互动进行了设计和开发，取得了良好的效果，从而设计了一种有效的矿山视景仿真技术。     

A heuristic‐based approach to mitigating positional errors in patrol data for species distribution modeling

Species distribution modeling (SDM) at fine spatial resolutions requires species occurrence data of high positional accuracy to achieve good model performance. However, wildlife occurrences recorded by patrols in ranger‐based monitoring programs suffer from positional errors, because recorded locations represent the positions of the ranger and differ from the actual occurrence locations of wildlife (hereinafter referred to as positional errors in patrol data). This study presented an evaluation of the impact of such positional errors in patrol data on SDM and developed a heuristic‐based approach to mitigating the positional errors. The approach derives probable wildlife occurrence locations from ranger positions, utilizing heuristics based on species preferred habitat and the observer's field of view. The evaluations were conducted through a case study of SDM using patrol records of the black‐and‐white snub‐nosed monkey (Rhinopithecus bieti) in Yunnan, China. The performance of the approach was also compared against alternative sampling methods. The results showed that the positional errors in R. bieti patrol data had an adverse effect on SDM performance, and that the proposed approach can effectively mitigate the impact of the positional errors to greatly improve SDM performance.     

基于部分最小二乘岭估计的粗差定值定位

在利用部分最小二乘原理进行粗差定值定位时,模型的法方程矩阵可能存在病态性,使得到的粗差定值定位结果不可靠。文中针对观测数据包含多个粗差且法方程病态问题,利用岭估计处理病态问题,建立部分最小二乘岭估计的粗差定值定位方法,给出粗差搜索步骤,利用迭代算法实现多个粗差的定值和定位。通过模拟算例分析部分最小二乘法、部分最小二乘岭估计在粗差搜索方面的效果,从另一个角度探讨粗差处理方法,推广现有的误差理论,证明文中方法的有效性。     

预建高精度地图的封闭区域UGV自动驾驶导航定位

针对封闭区域UGV自动驾驶应用,本文提出了一种基于平面高精度地图的导航定位方法。该方法利用三维激光扫描数据采用预定义的栅格地图概率值建立多分辨率地图,在保证定位精度的同时提高定位效率,采用极大似然估计获取载体位姿初值并将IMU数据用于计算高斯-牛顿法搜索初值。试验结果表明,基于激光扫描的二维地图构建与匹配定位方法能有效解决帧与帧匹配误差快速累积问题,可以高效地使用已有地图进行连续高精度的载体定位。     

Urban event detection with big data of taxi OD trips: A time series decomposition approach

Big urban mobility data, such as taxi trips, cell phone records, and geo‐social media check‐ins, offer great opportunities for analyzing the dynamics, events, and spatiotemporal trends of the urban social landscape. In this article, we present a new approach to the detection of urban events based on location‐specific time series decomposition and outlier detection. The approach first extracts long‐term temporal trends and seasonal periodicity patterns. Events are defined as anomalies that deviate significantly from the prediction with the discovered temporal patterns, i.e., trend and periodicity. Specifically, we adopt the STL approach, i.e., seasonal and trend decomposition using LOESS (locally weighted scatterplot smoothing), to decompose the time series for each location into three components: long‐term trend, seasonal periodicity, and the remainder. Events are extracted from the remainder component for each location with an outlier detection method. We analyze over a billion taxi trips for over seven years in Manhattan (New York City) to detect and map urban events at different temporal resolutions. Results show that the approach is effective and robust in detecting events and revealing urban dynamics with both holistic understandings and location‐specific interpretations.     

Thinking about Space‐Time Connections: Spatiotemporal Scheduling of Individual Activities

This article presents a spatiotemporal model for scheduling applications that is driven by the events and activities individuals plan and manage every day. The framework is presented using an ontological approach where ontologies at different levels of generalization, e.g. domain, application, and task ontologies, are linked together through participation and inheritance relationships. S_Events are entered into a schedule as a new S_Entry, or modifications can be made to existing entries including reschedule, postpone, change location, and delete as schedules vary over time. These schedule updates are formalized through changes to planned start and end times and the planned locations of S_Entries are expressed using SWRL, a semantic web rule language. SWRL is also used for reasoning about schedule changes and the space‐time conflicts that can occur. The sequence of entries in a schedule gives rise to S_trajectories representing the locations that individuals plan to visit in order to carry out their schedule, adding an additional spatial element to the framework. A prototype Geoscheduler application maps S_Entries against a timeline, offering a spatiotemporal visualization of scheduled activities showing the evolution of a schedule over space‐time and affecting spatiotemporal accessibility for individuals.     

Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records

Understanding the spatiotemporal dynamics of urban population is crucial for addressing a wide range of urban planning and management issues. Aggregated geospatial big data have been widely used to quantitatively estimate population distribution at fine spatial scales over a given time period. However, it is still a challenge to estimate population density at a fine temporal resolution over a large geographical space, mainly due to the temporal asynchrony of population movement and the challenges to acquiring a complete individual movement record. In this article, we propose a method to estimate hourly population density by examining the time‐series individual trajectories, which were reconstructed from call detail records using BP neural networks. We first used BP neural networks to predict the positions of mobile phone users at an hourly interval and then estimated the hourly population density using log‐linear regression at the cell tower level. The estimated population density is linearly correlated with population census data at the sub‐district level. Trajectory clustering results show five distinct diurnal dynamic patterns of population movement in the study area, revealing spatially explicit characteristics of the diurnal commuting flows, though the driving forces of the flows need further investigation.     

Modeling Alternative Sequences of Events in Dynamic Geographic Domains

A model that emphasizes possible alternative sequences of events that occur over time is presented in this article. Representing alternative or branching events captures additional semantics unrealized by linear or non‐branching approaches. Two basic elements of branching, divergence and convergence are discussed. From these elements, many complex branching models can be built capturing a perspective of events that will take place in the future or have occurred in the past. This produces likely sequences of events that a user may compare and analyze using spatial or temporal criteria. The branching events model is especially useful for spatiotemporal decision support systems, as decision‐makers are able to identify alternative locations and times of events and, depending on the context, also identify regions of multiple possible events. A branching events viewer application is presented illustrating a case study based on a tornado response.